Computational steering strategy to calibrate input variables in a dynamic data driven genetic algorithm for forest fire spread prediction

Mónica Denham*, Ana Cortés, Tomás Margalef

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

14 Citations (Scopus)

Abstract

This work describes a Dynamic Data Driven Genetic Algorithm (DDDGA) for improving wildfires evolution prediction. We propose an universal computational steering strategy to automatically adjust certain input data values of forest fire simulators, which works independently on the underlying propagation model. This method has been implemented in a parallel fashion and the experiments performed demonstrated its ability to overcome the input data uncertainty and to reduce the execution time of the whole prediction process.

Original languageAmerican English
Pages (from-to)479-488
Number of pages10
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Issue numberPART 2
DOIs
Publication statusPublished - 2009

Fingerprint Dive into the research topics of 'Computational steering strategy to calibrate input variables in a dynamic data driven genetic algorithm for forest fire spread prediction'. Together they form a unique fingerprint.

Cite this